Índices de tolerância e abordagem fatorial analítico no estudo do déficit hídrico em milho

• Autor(a) principal: Santos, Álvaro de Oliveira
• Data de Publicação: 2016
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/11676
• topic: Milho - Produtividade agrícola; Cultivos agrícolas – Rendimento; Estresse hídrico; Corn - Agricultural productivity; Crop yields; Water stress; Zea mays; Fitotecnia

Water stress is the abiotic factor with the highest impact over the reduction of corn grain productivity. The search for solutions that minimize these effects have been investigated. The objective of this work was to evaluate the performance of corn hybrids in environments with and without water restriction, by means of tolerance indexes and approach via analytical factor (AF). We used 171 corn hybrids, in environments with and without water restriction, during many years. The characteristics of potential grain productivity (PGp), grain productivity under water stress (PGs), male (MF) and female (FF) flowering, plant height (PH) and cob height (CH) were evaluated. The susceptibility to drought index (SDI), water stress tolerance (WST), resistance to drought coefficient (DC), resistance to water stress coefficient (RSC), water stress tolerance index (STI) and harmonic mean (HM) were also evaluated. The adaptability and stability were verified by means of the AF model of the hybrids. There was difference between the hybrids and environments for all evaluated characteristics. The TSI and HM indexes may be used for evaluating and identifying corn hybrids more productive in environments with and without water restriction. Hybrids G1, G18 and G35 were the most stable and presented potential for use in environments with and without water restriction. The AF models are useful for evaluating hybrids over many years, allowing the selection of the best hybrids with adaptability and specific and wide stability and relate the hybrids to its production components, in addition to allowing the identification of mega environments.